Aortic stenosis (AS) is the most common valvular heart disease in developed countries. High-fidelity preclinical models can improve AS management by enabling therapeutic innovation, early diagnosis, and tailored treatment planning. However, their use is currently limited by complex workflows necessitating lengthy expert-driven manual operations. Here, we propose an AI-powered computational framework for accelerated and democratized patient-specific modeling of AS hemodynamics from computed tomography. First, we demonstrate that our automated meshing algorithms can generate task-ready geometries for both computational and benchtop simulations with higher accuracy and 100 times faster than existing approaches. Then, we show that our approach can be integrated with fluid-structure interaction and soft robotics models to accurately recapitulate a broad spectrum of clinical hemodynamic measurements of diverse AS patients. The efficiency and reliability of these algorithms make them an ideal complementary tool for personalized high-fidelity modeling of AS biomechanics, hemodynamics, and treatment planning.

我们提出了一种人工智能驱动的计算框架，用于加速与民主化的基于患者的主动脉瓣狭窄血流动力学模拟，通过自动网格化算法生成适用于计算和实验室模拟的几何形态，并能够准确地模拟多样化的临床血流动力学测量，从而作为个性化高保真模拟主动脉瓣狭窄生物力学、血流动力学和治疗规划的理想补充工具。

基于AI的主题建模的个性化主动脉狭窄血动力学